Smoothed Wma Z-score | viResearchSmoothed Wma Z-score | viResearch
Conceptual Foundation and Innovation
The "Smoothed Wma Z-score" indicator from viResearch integrates the Weighted Moving Average (WMA) with Z-score analysis, providing traders with a precise tool for identifying market extremes and potential reversions. The WMA gives more weight to recent data, making it highly responsive to short-term price fluctuations, while the Z-score standardizes this price action relative to its historical mean and volatility. By smoothing the WMA and applying Z-score analysis, this indicator helps traders detect when the market is either overbought or oversold, offering actionable signals for mean reversion or trend continuation strategies.
The combination of WMA smoothing and Z-score analysis allows traders to better evaluate the strength of market trends while pinpointing moments when price may be stretched beyond its typical range.
Technical Composition and Calculation
The "Smoothed Wma Z-score" script consists of two primary components: the Weighted Moving Average (WMA) and the Z-score. The WMA is calculated using a user-defined period, applying more weight to recent price data to provide a smoothed representation of the price trend. The Z-score is then derived by measuring how far the current WMA deviates from its historical mean, normalized by its standard deviation over a specified lookback period. This calculation gives a standardized measure of price extremes, allowing traders to determine whether the current price is statistically far from its norm.
The script compares the Z-score with customizable threshold levels to generate buy and sell signals. A Z-score exceeding the upper threshold suggests potential overbought conditions, while a Z-score below the lower threshold may indicate oversold conditions. Additionally, the script highlights areas where price is in the "mean reversion zone," helping traders anticipate when price might revert back to its average.
Features and User Inputs
The "Smoothed Wma Z-score" script offers several customizable inputs, enabling traders to tailor the indicator to their specific trading strategies. The WMA Length determines the sensitivity of the WMA to price changes, while the Lookback Period controls the range over which the mean and standard deviation of the WMA are calculated for the Z-score. Traders can also adjust the thresholds to define the sensitivity of overbought and oversold conditions. Furthermore, the script includes alert conditions that notify traders when trend shifts occur, allowing for timely responses to market movements.
Practical Applications
The "Smoothed Wma Z-score" indicator is designed for traders who focus on identifying price extremes and potential mean reversion opportunities. By combining WMA smoothing with Z-score analysis, this tool can be particularly effective for detecting points of overextension in the market, where a reversion to the mean is likely. The indicator is valuable for traders who seek to capitalize on:
Detecting Overbought and Oversold Conditions: The Z-score measures how far the price has deviated from its norm, allowing traders to identify overbought or oversold conditions with precision. Timing Market Reversals: The indicator provides early signals of potential market reversals by highlighting when the price has moved too far away from its average, helping traders anticipate reversion opportunities. Improving Trend Continuation Strategies: The WMA’s responsiveness to recent price changes, combined with the Z-score’s ability to measure deviations, offers traders a clearer understanding of whether a trend is likely to continue or if it’s overextended.
Advantages and Strategic Value
The "Smoothed Wma Z-score" script provides significant value by integrating WMA smoothing with Z-score analysis, delivering a powerful combination for traders seeking to identify extreme price movements. The ability to smooth price data while detecting statistically significant deviations ensures that traders are better equipped to spot reversals or continuation signals. This dual approach helps reduce noise in price data while offering a robust method for timing entries and exits, making the "Smoothed Wma Z-score" a versatile tool for both mean reversion and trend-following strategies.
Alerts and Visual Cues
The script includes alert conditions that notify traders when key thresholds are crossed. The "Smoothed Wma Z-score Long" alert is triggered when the Z-score moves above the upper threshold, signaling potential overbought conditions. The "Smoothed Wma Z-score Short" alert is activated when the Z-score drops below the lower threshold, indicating possible oversold conditions. Visual cues, such as color changes in the Z-score plot and highlighted mean reversion zones, help traders quickly identify critical market conditions and make timely decisions.
Summary and Usage Tips
The "Smoothed Wma Z-score | viResearch" indicator provides traders with a powerful tool for analyzing price extremes and identifying mean reversion opportunities. By incorporating this script into your trading strategy, you can improve your ability to spot overbought and oversold conditions, timing market reversals with greater accuracy. The "Smoothed Wma Z-score" is a reliable and customizable solution for traders focused on both mean reversion and trend-following strategies in volatile market environments.
Note: Backtests are based on past results and are not indicative of future performance.
Statistics
Solar System in 3D [Astro Tool w/ Zodiac]Hello Traders and Developers,
I am excited to announce my latest Open Source indicator. At the core, this is a demonstration of PineScript’s capabilities in Rendering 3D Animations, while at the same time being a practical tool for Financial Astrologists.
This 3D Engine dynamically renders all the major celestial bodies with their individual orbits, rotation speeds, polar inclinations and astrological aspects, all while maintaining accurate spatial relationships and perspective.
This is a Geocentric model of the solar system (viewed from the perspective of Earth), since that is what most Astrologists use. Thanks to the AstroLib Library created by @BarefootJoey, this model uses the real coordinates of cosmic bodies for every timestamp.
This script truly comes to life when using the “Bar Replay” mode in TradingView, as you can observe the relationships between planets and price action as time progresses, with the full animation capabilities as mentioned above.
In addition to what I have described, this indicator also displays the orbital trajectories for each cosmic body, and has labels for everything. I have also added the ability to hover on all the labels, and see a short description of what they imply in Astrology.
Optional Planetary Aspect Computation
This indicator supports all the Major Planetary Aspects, with an accuracy defined by the user (1° by default).
Conjunction: 0° Alignment. This draws a RED line starting from the center, and going through both planets.
Sextile: 60° Alignment. This draws three YELLOW lines, connecting the planets to each other and to the center.
Square: 90° Alignment. This draws three BLUE lines, connecting the planets to each other and to the center.
Trine: 120° Alignment. This draws three PURPLE lines, connecting the planets to each other and to the center.
Opposition: 180° Alignment. This draws a GREEN line starting from one planet, passing through the center and ending on the second planet.
The below image depicts a Top-Down view of the system, with the Moon in Opposition to Venus and with Mars in Square with Neptune .
Retrograde Computation
This indicator also displays when a planet enters Retrograde (Apparent Backward Motion) by making its orbital trajectory dashed and the planet name getting a red background.
The image below displays an example of Jupiter, Saturn, Neptune and Pluto in Retrograde Motion, from the camera perspective of a 65 degree inclination.
Optional Zodiac Computation (Tropical and Sidereal)
Zodiac represents the relatively stationary star formations that rest along the ecliptic plane, with planets transitioning from one to the next, each with a 30° separation (making 12 in total). I have implemented the option to switch between Tropical mode (where these stars were 2,000 years ago) and Sidereal (where these stars are today).
The image below displays the Zodiac labels with clear lines denoting where each planet falls into.
While this indicator is deployed in a separate pane, it is trivial to transfer it onto your price chart, just by clicking and dragging the graphics. After that, you can adjust the visuals by dragging the scale on the side, or optimizing model settings. You can also drag the model above or below the price, as shown in the following image:
Of course, there are a lot of options to customize this planetary model to your tastes and analytical needs. Aside from visual changes for the labels, colors or resolution you can also disable certain planets that don’t meet your needs as shown below:
Once can also infer the current lunar phases using the Aspects between the Sun and Moon. When the Moon is Opposite the Sun that is a Full Moon, while when they are Conjunct that is a New Moon (and sometimes Eclipse).
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I have made this indicator open source to help PineScript programmers understand how to approach 3D graphics rendering, enabling them to develop ever more capable scripts and continuously push the boundaries of what's possible on TradingView.
The code is well documented with comments and has a clear naming convention for functions and variables, to aid developers understand how everything operates.
For financial astrologists, this indicator offers a new way to visualize and correlate planetary movements, adding depth and ease to astrological market analysis.
Regards,
Hawk
[DarkTrader] Swing OrderflowSwing Orderflow is a indicator that helps traders detect key swing highs and lows in price action. It is designed to enhance your charting experience by highlighting important support and resistance levels while providing clear visual cues on market structure changes. By tracking swing pivots and price trends, this indicator enables traders to make more informed decisions regarding potential entry and exit points in the market.
This indicator is inspired by @Fractalyst Indicator :
The core functionality of the script revolves around detecting swing highs and lows based on a customizable swing period. It identifies these pivots by comparing price movements over a specific time window, marking the points where price either peaks or bottoms out. Swing highs are plotted as resistance levels when the price breaks above a certain threshold, while swing lows are plotted as support levels when price breaks below it. These key points are represented with dotted lines and labels on the chart for easy reference.
Indicator In Use :
Swing High Calculation
A swing high occurs when the high of a specific bar (or candle) is greater than the highs of the surrounding bars within a defined range (called the swing period).
Function used to find the highest price within a specified range : ta.highest(period)
If the current price is greater than the highest price of this period, it's marked as a potential swing high.
A swing high generally represents a resistance level, where the price has reached a peak before declining.
Swing Low Calculation
A swing low occurs when the low of a specific bar is lower than the lows of the surrounding bars within the swing period.
Function used to find the lowest price within a specified range : ta.lowest(period)
If the current price is lower than this lowest price, it's identified as a swing low.
Swing lows represent support levels, where the price reaches a bottom before bouncing back.
These points are plotted on the chart, and the script also tracks whether price breaks above the swing high or below the swing low to determine trends or possible reversals.
BSL (Buy Side Liquidity)
BSL represents the Buy Side Liquidity, where traders are expected to have their buy orders (usually stop-loss orders for short positions).
When the price reaches a swing high, traders who are short may have stop orders placed above this level. Once these levels are breached, the script identifies this as a liquidity area where stop orders get triggered, causing potential upward price movement.
The script marks the swing high with a "BSL" label and line to indicate this key resistance and liquidity zone.
SSL (Sell Side Liquidity)
SSL refers to the Sell Side Liquidity, where traders place sell orders (usually stop-loss orders for long positions).
Swing lows are important levels where traders holding long positions place their stop orders. When the price drops below a swing low, it triggers these sell orders, causing potential downward price movement.
The script marks the swing low with an "SSL" label and line, signaling this key support and liquidity zone.
In essence, BSL and SSL represent areas where liquidity is pooled, making them critical points in price action. These liquidity areas, when breached, often lead to aggressive price moves, allowing traders to anticipate trends.
[ALGOA+] Markov Chains Library by @metacamaleoLibrary "MarkovChains"
Markov Chains library by @metacamaleo. Created in 09/08/2024.
This library provides tools to calculate and visualize Markov Chain-based transition matrices and probabilities. This library supports two primary algorithms: a rolling window Markov Chain and a conditional Markov Chain (which operates based on specified conditions). The key concepts used include Markov Chain states, transition matrices, and future state probabilities based on past market conditions or indicators.
Key functions:
- `mc_rw()`: Builds a transition matrix using a rolling window Markov Chain, calculating probabilities based on a fixed length of historical data.
- `mc_cond()`: Builds a conditional Markov Chain transition matrix, calculating probabilities based on the current market condition or indicator state.
Basically, you will just need to use the above functions on your script to default outputs and displays.
Exported UDTs include:
- s_map: An UDT variable used to store a map with dummy states, i.e., if possible states are bullish, bearish, and neutral, and current is bullish, it will be stored
in a map with following keys and values: "bullish", 1; "bearish", 0; and "neutral", 0. You will only use it to customize your own script, otherwise, it´s only for internal use.
- mc_states: This UDT variable stores user inputs, calculations and MC outputs. As the above, you don´t need to use it, but you may get features to customize your own script.
For example, you may use mc.tm to get the transition matrix, or the prob map to customize the display. As you see, functions are all based on mc_states UDT. The s_map UDT is used within mc_states´s s array.
Optional exported functions include:
- `mc_table()`: Displays the transition matrix in a table format on the chart for easy visualization of the probabilities.
- `display_list()`: Displays a map (or array) of string and float/int values in a table format, used for showing transition counts or probabilities.
- `mc_prob()`: Calculates and displays probabilities for a given number of future bars based on the current state in the Markov Chain.
- `mc_all_states_prob()`: Calculates probabilities for all states for future bars, considering all possible transitions.
The above functions may be used to customize your outputs. Use the returned variable mc_states from mc_rw() and mc_cond() to display each of its matrix, maps or arrays using mc_table() (for matrices) and display_list() (for maps and arrays) if you desire to debug or track the calculation process.
See the examples in the end of this script.
Have good trading days!
Best regards,
@metacamaleo
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KEY FUNCTIONS
mc_rw(state, length, states, pred_length, show_table, show_prob, table_position, prob_position, font_size)
Builds the transition matrix for a rolling window Markov Chain.
Parameters:
state (string) : The current state of the market or system.
length (int) : The rolling window size.
states (array) : Array of strings representing the possible states in the Markov Chain.
pred_length (int) : The number of bars to predict into the future.
show_table (bool) : Boolean to show or hide the transition matrix table.
show_prob (bool) : Boolean to show or hide the probability table.
table_position (string) : Position of the transition matrix table on the chart.
prob_position (string) : Position of the probability list on the chart.
font_size (string) : Size of the table font.
Returns: The transition matrix and probabilities for future states.
mc_cond(state, condition, states, pred_length, show_table, show_prob, table_position, prob_position, font_size)
Builds the transition matrix for conditional Markov Chains.
Parameters:
state (string) : The current state of the market or system.
condition (string) : A string representing the condition.
states (array) : Array of strings representing the possible states in the Markov Chain.
pred_length (int) : The number of bars to predict into the future.
show_table (bool) : Boolean to show or hide the transition matrix table.
show_prob (bool) : Boolean to show or hide the probability table.
table_position (string) : Position of the transition matrix table on the chart.
prob_position (string) : Position of the probability list on the chart.
font_size (string) : Size of the table font.
Returns: The transition matrix and probabilities for future states based on the HMM.
Investments Follower Table
🔸 Investments Follower Table is a useful tool to visualize the performance of your investments, regardless of which graph you are viewing.
🔸 By entering the name and the quantities of the stock you own, and your average purchase price, you can view the percentage changes and the profit/loss of your investments, regardless of the graph you’re viewing.
🔸 This indicator allows you to follow 4 stocks at the same time.
🔸 When you first open the indicator, you won’t see the table until you’ll fill the fields necessary to show the data.
🔸 Explanation of the menu:
- % TABLE toggle: Turns On and Off the table
- $ TABLE toggle: This function removes the column of the profit or loss in $. The percentage one will be kept.
- TICKER 1/2/3/4: It’s the part of the settings in which you put the name of the share you own.
- ONLY VALUES toggle: Reduces the table by removing the name of the ticker.
- REDUCED TABLE toggle: Removes all the rows of the table associated to the single investment, to just keep the summery of all the investments active
🔸If you turn off a toggle, but keeping some values inside the menu, the table will also consider that values in the calculation of the summery into the table. To avoid this, you just need to put in the A.P.P. and in the QTY the value “0”.
🔸If you want to reset the indicator, you just have to click “Defaults” in the bottom left corner, and then “Reset settings”.
Stoch RSI Time StatisticsThe “Stochastic RSI Time Statistics” is a comprehensive tool designed to enhance your trading decisions by combining the traditional Stochastic RSI with additional metrics and visual aids. This indicator can be used to detect overbought and oversold conditions, issue long and short alerts based on crossovers, and help you analyse market movements by providing detailed statistical insights.
The Stochastic RSI is an open source script that was developed by Tushar Chande and Stanley Kroll and introduced in their book "The New Technical Trader" in 1994. It combines two popular indicators: the “Relative Strength Index (RSI)” and the “Stochastic Oscillator”.
The “Stochastic RSI Time Statistics” uses the stochastic RSI calculations and additionally calculates various probability and frequency statistics to better understand the momentum oscillator’s behaviour and guide our strategies and risk management.
Statistics & Probabilities:
The indicator calculates important time and frequency-based metrics that provide deeper insight into the behaviour of the Stochastic RSI. These are displayed in a text box on the indicator panel, including:
Avg Long: The average number of bars between the last long signal before exiting the critical zone and the next short signal in the overbought critical zone, including the standard deviation and the sample size within the relevant time frame.
Avg Short: The average number of bars between the last short signal before exiting the critical zone and the next long signal in the oversold critical zone, including the standard deviation and the sample size within the relevant time frame.
Avg Consecutive Longs: The average number of consecutive long signals before the first proceeding short signal occurs, with standard deviation.
Avg Consecutive Shorts: The average number of consecutive short signals before the first proceeding long signal occurs, with standard deviation.
Time in Oversold: The average time (in bars/candle sticks) that the Stochastic RSI lines (K & D Lines both in critical zone) spends in the oversold region (below the buy signal level) after entering the oversold region and until both K & D lines depart from the oversold region, along with the standard deviation.
Time in Overbought: The average time (in bars/candle sticks) that the Stochastic RSI lines (K & D Lines both in critical zone) spends in the overbought region (above the sell signal level), after entering the overbought region and until both K & D lines depart from the overbought region, along with the standard deviation.
Signal Frequency: It calculates the percentage of a single, double, triple, and more than triple long or short signals that occur consecutively within the critical zone before the opposing signal occurs (e.g., 1Long: 40.54%, 2 Long:28.55%, 3Long 17.4%, >3 Long:13.51%, 1Short:36.15%, 2Short:30.41%, 3Short:17.57%, >3Short:15.88%).
Key Features:
Oversold: When the Stochastic RSI is below 20, it indicates that the RSI is in a low range, and the asset may be oversold, potentially signalling a buying opportunity.
Overbought: When the Stochastic RSI is above 80, it suggests the RSI is in a high range, meaning the asset may be overbought and a downturn might be near.
The Stochastic RSI Slope indicates the prominent trend direction within a relevant time period.
Customizable Buy Signal Level (typically below 20-25 percentile) to detect oversold conditions. Customizable Sell Signal Level (typically above 75-80 percentile) to detect overbought conditions. These levels help you spot potential reversal zones where long or short trades might be initiated.
Crossover Alerts:
The indicator tracks crossovers between the K and D lines, generating long signals when a crossover occurs below the buy signal level (indicating oversold conditions) and short signals when a cross under occurs above the sell signal level (indicating overbought conditions). The signals are visualized as labels on the chart:
**L** for potential long (buy) signals: Marked below the bars when the K line crosses above the D line.
**S** for potential short (sell) signals: Marked above the bars when the K line crosses below the D line.
Visual Alerts are generated based on these signals.
Risk Management
Although the Stochastic RSI is typically regarded as presenting trend direction and overbought and oversold conditions when in the extreme zones, the RSI can linger and cross over or under numerous times while in the critical zone. The statistics added to the Stochastic RSI indicator allows one to assess the statistical probability of numerous crossover signals occurring on an asset or at various time frames. Signal levels, or preferred definitions of the critical zones can be adjusted while the statistics are automatically updated to the relevant ticker or time frame. Colours and Signal shapes are adjustable to suite your visual preferences.
By using this indicator, you acknowledge and agree that:
No Guarantees: The indicator is provided "as-is" without any warranties or guarantees of accuracy, completeness, or fitness for a particular purpose. The outcomes or performance of trades executed using this indicator are not guaranteed to be successful or profitable.
User Responsibility: You are solely responsible for any trading decisions you make based on the use of this indicator. All trading and investment activities involve risk, and it is essential to conduct your own research, analysis, and due diligence before making any financial decisions.
No Liability: The creator of this indicator is not responsible for any financial losses, direct or indirect, incurred as a result of using this indicator. This includes, but is not limited to, loss of profits, loss of capital, or any other negative financial outcomes.
Market Risks: Markets are volatile, and prices may fluctuate significantly. Trading and investing carry inherent risks, and there is always the potential for loss. You should only trade with capital that you can afford to lose.
Independent Advice: It is strongly recommended that you seek independent financial advice from a qualified and licensed professional before making any trading or investment decisions based on the use of this indicator.
By using this indicator, you acknowledge that you fully understand and accept the risks involved, and you agree to indemnify and hold harmless the creator of this indicator from any claims, damages, or liabilities arising from its use.
The author of this script has made every effort to ensure that the code is an original interpretation and application of the open-source Stochastic RSI, as developed by the original authors, Tushar Chande and Stanley Kroll. The script reflects a unique adaptation aimed at enhancing trading strategies through advanced statistical analysis and trade management features. The author does not claim any proprietary rights over the foundational concepts of the Stochastic RSI and does not intend to infringe upon any existing copyrights. Should any copyright infringement be identified, the author commits to removing the indicator immediately and forfeits any rights to further or intended financial gain from its use.
OrderFlow [Adjustable] | FractalystWhat's the indicator's purpose and functionality?
This indicator is designed to assist traders in identifying real-time probabilities of buyside and sellside liquidity .
It allows for an adjustable pivot level , enabling traders to customize the level they want to use for their entries.
By doing so, traders can evaluate whether their chosen entry point would yield a positive expected value over a large sample size, optimizing their strategy for long-term profitability.
For advanced traders looking to enhance their analysis, the indicator supports the incorporation of up to 7 higher timeframe biases .
Additionally, the higher timeframe pivot level can be adjusted according to the trader's preferences,
Offering maximum adaptability to different strategies and needs, further helping to maximize positive EV.
EV=(P(Win)×R(Win))−(P(Loss)×R(Loss))
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What's the purpose of these levels? What are the underlying calculations?
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Pivot levels.
3. Identifying Discount and Premium Zones.
4. Importance of Risk-Reward in Premium and Discount Ranges
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How does the script calculate probabilities?
The script calculates the probability of each liquidity level individually. Here's the breakdown:
1. Upon the formation of a new range, the script waits for the price to reach and tap into pivot level level. Status: "⏸" - Inactive
2. Once pivot level is tapped into, the pivot status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
4. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
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What does the multi-timeframe functionality offer?
In the adjustable version of the orderflow indicator, you can incorporate up to 7 higher timeframe probabilities directly into the table.
This feature allows you to analyze the probabilities of buyside and sellside liquidity across multiple timeframes, without the need to manually switch between them.
By viewing these higher timeframe probabilities in one place, traders can spot larger market trends and refine their entries and exits with a better understanding of the overall market context.
This multi-timeframe functionality helps traders:
1. Simplify decision-making by offering a comprehensive view of multiple timeframes at once.
2. Identify confluence between timeframes, enhancing the confidence in trade setups.
3. Adapt strategies more effectively, as the higher timeframe pivot levels can be customized to meet individual preferences and goals.
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What are the multi-timeframe underlying calculations?
The script uses the same calculations (mentioned above) and uses security function to request the data such as price levels, bar time, probabilities and booleans from the user-input timeframe.
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How does the Indicator Identifies Positive Expected Values?
OrderFlow indicator instantly calculates whether a trade setup has the potential for positive expected value (EV) in the long run.
To determine a positive EV setup, the indicator uses the formula:
EV=(P(Win)×R(Win))−(P(Loss)×R(Loss))
where:
P(Win) is the probability of a winning trade.
R(Win) is the reward or return for a winning trade, determined by the current risk-to-reward ratio (RR).
P(Loss) is the probability of a losing trade.
R(Loss) is the loss incurred per losing trade, typically assumed to be -1.
By calculating these values based on historical data and the current trading setup, the indicator helps you understand whether your trade has a positive expected value over a large sample size.
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How can I know that the setup I'm going to trade with has a postive EV?
If the indicator detects that the adjusted pivot and buy/sell side probabilities have generated positive expected value (EV) in historical data, the risk-to-reward (RR) label within the range box will be colored blue and red .
If the setup does not produce positive EV, the RR label will appear gray.
This indicates that even the risk-to-reward ratio is greater than 1:1, the setup is not likely to yield a positive EV because, according to historical data, the number of losses outweighs the number of wins relative to the RR gain per winning trade.
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What is the confidence level in the indicator, and how is it determined?
The confidence level in the indicator reflects the reliability of the probabilities calculated based on historical data. It is determined by the sample size of the probabilities used in the calculations. A larger sample size generally increases the confidence level, indicating that the probabilities are more reliable and consistent with past performance.
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How does the confidence level affect the risk-to-reward (RR) label?
The confidence level (★) is visually represented alongside the probability label. A higher confidence level indicates that the probabilities used to determine the RR label are based on a larger and more reliable sample size.
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How can traders use the confidence level to make better trading decisions?
Traders can use the confidence level to gauge the reliability of the probabilities and expected value (EV) calculations provided by the indicator. A confidence level above 95% is considered statistically significant and indicates that the historical data supporting the probabilities is robust. This high confidence level suggests that the probabilities are reliable and that the indicator’s recommendations are more likely to be accurate.
In data science and statistics, a confidence level above 95% generally means that there is less than a 5% chance that the observed results are due to random variation. This threshold is widely accepted in research and industry as a marker of statistical significance. Studies such as those published in the Journal of Statistical Software and the American Statistical Association support this threshold, emphasizing that a confidence level above 95% provides a strong assurance of data reliability and validity.
Conversely, a confidence level below 95% indicates that the sample size may be insufficient and that the data might be less reliable . In such cases, traders should approach the indicator’s recommendations with caution and consider additional factors or further analysis before making trading decisions.
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How does the sample size affect the confidence level, and how does it relate to my TradingView plan?
The sample size for calculating the confidence level is directly influenced by the amount of historical data available on your charts. A larger sample size typically leads to more reliable probabilities and higher confidence levels.
Here’s how the TradingView plans affect your data access:
Essential Plan
The Essential Plan provides basic data access with a limited amount of historical data. This can lead to smaller sample sizes and lower confidence levels, which may weaken the robustness of your probability calculations. Suitable for casual traders who do not require extensive historical analysis.
Plus Plan
The Plus Plan offers more historical data than the Essential Plan, allowing for larger sample sizes and more accurate confidence levels. This enhancement improves the reliability of indicator calculations. This plan is ideal for more active traders looking to refine their strategies with better data.
Premium Plan
The Premium Plan grants access to extensive historical data, enabling the largest sample sizes and the highest confidence levels. This plan provides the most reliable data for accurate calculations, with up to 20,000 historical bars available for analysis. It is designed for serious traders who need comprehensive data for in-depth market analysis.
PRO+ Plans
The PRO+ Plans offer the most extensive historical data, allowing for the largest sample sizes and the highest confidence levels. These plans are tailored for professional traders who require advanced features and significant historical data to support their trading strategies effectively.
For many traders, the Premium Plan offers a good balance of affordability and sufficient sample size for accurate confidence levels.
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What is the HTF probability table and how does it work?
The HTF (Higher Time Frame) probability table is a feature that allows you to view buy and sellside probabilities and their status from timeframes higher than your current chart timeframe.
Here’s how it works:
Data Request : The table requests and retrieves data from user-defined higher timeframes (HTFs) that you select.
Probability Display: It displays the buy and sellside probabilities for each of these HTFs, providing insights into the likelihood of price movements based on higher timeframe data.
Detailed Tooltips: The table includes detailed tooltips for each timeframe, offering additional context and explanations to help you understand the data better.
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What do the different colors in the HTF probability table indicate?
The colors in the HTF probability table provide visual cues about the expected value (EV) of trading setups based on higher timeframe probabilities:
Blue: Suggests that entering a long position from the HTF user-defined pivot point, targeting buyside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Red: Indicates that entering a short position from the HTF user-defined pivot point, targeting sellside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Gray: Shows that neither long nor short trades from the HTF user-defined pivot point are expected to generate positive EV, suggesting that trading these setups may not be favorable.
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How to use the indicator effectively?
For Amateur Traders:
Start Simple: Begin by focusing on one timeframe at a time with the pivot level set to the default (50%). This helps you understand the basic functionality of the indicator.
Entry and Exit Strategy: Focus on entering trades at the pivot level while targeting the higher probability side for take profit and the lower probability side for stop loss.
Use simulation or paper trading to practice this strategy.
Adjustments: Once you have a solid understanding of how the indicator works, you can start adjusting the pivot level to other values that suit your strategy.
Ensure that the RR labels are colored (blue or red) to indicate positive EV setups before executing trades.
For Advanced Traders:
1. Select Higher Timeframe Bias: Choose a higher timeframe (HTF) as your main bias. Start with the default pivot level and ensure the confidence level is above 95% to validate the probabilities.
2. Align Lower Timeframes: Switch between lower timeframes to identify which ones align with your predefined HTF bias. This helps in synchronizing your trading decisions across different timeframes.
3. Set Entries with Current Pivot Level: Use the current pivot level for trade entries. Ensure the HTF status label is active, indicating that the probabilities are valid and in play.
4. Target HTF Liquidity Level: Aim for liquidity levels that correspond to the higher timeframe, as these levels are likely to offer better trading opportunities.
5. Adjust Pivot Levels: As you gain experience, adjust the pivot levels to further optimize your strategy for high EV. Fine-tune these levels based on the aggregated data from multiple timeframes.
6. Practice on Paper Trading: Test your strategies through paper trading to eliminate discretion and refine your approach without financial risk.
7. Focus on Trade Management: Ultimately, effective trade management is crucial. Concentrate on managing your trades well to ensure long-term success. By aiming for setups that produce positive EV, you can position yourself similarly to how a casino operates.
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🎲 Becoming the House (Gaining Edge Over the Market):
In American roulette, the house has a 5.26% edge due to the 0 and 00. This means that while players have a 47.37% chance of winning on even-money bets, the true odds are 50%. The discrepancy between the true odds and the payout ensures that, statistically, the casino will win over time.
From the Trader's Perspective: In trading, you gain an edge by focusing on setups with positive expected value (EV). If you have a 55.48% chance of winning with a 1:1 risk-to-reward ratio, your setup has a higher probability of profitability than the losing side. By consistently targeting such setups and managing your trades effectively, you create a statistical advantage, similar to the casino’s edge.
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🎰 Applying the Concept to Trading:
Just as casinos rely on their mathematical edge, you can achieve long-term success in trading by focusing on setups with positive EV. By ensuring that your probabilities and risk-to-reward (RR) ratios are in your favor, you create an edge similar to that of the house.
And by systematically targeting trades with favorable probabilities and managing your trades effectively, you improve your chances of profitability over the long run. Which is going to help you “become the house” in your trading, leveraging statistical advantages to enhance your overall performance.
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What makes this indicator original?
Real-Time Probability Calculations: The indicator provides real-time calculations of buy and sell probabilities based on historical data, allowing traders to assess the likelihood of positive expected value (EV) setups instantly.
Adjustable Pivot Levels: It features an adjustable pivot level that traders can modify according to their preferences, enhancing the flexibility to align with different trading strategies.
Multi-Timeframe Integration: The indicator supports up to 7 higher timeframes, displaying their probabilities and biases in a single view, which helps traders make informed decisions without switching timeframes.
Confidence Levels: It includes confidence levels based on sample sizes, offering insights into the reliability of the probabilities. Traders can gauge the strength of the data before making trades.
Dynamic EV Labels: The indicator provides color-coded EV labels that change based on the validity of the setup. Blue indicates positive EV in a long bias, red indicates positive EV in a short bias and gray signals caution, making it easier for traders to identify high-quality setups.
HTF Probability Table: The HTF probability table displays buy and sell probabilities from user-defined higher timeframes, helping traders integrate broader market context into their decision-making process.
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Ema Z-score | viResearchEma Z-score | viResearch
Conceptual Foundation and Innovation
The "Ema Z-score" indicator introduces a novel method of analyzing price deviations from the mean by combining the Exponential Moving Average (EMA) with a Z-score calculation. The Z-score is a statistical measure that quantifies how far a value deviates from the mean in terms of standard deviations. By applying the Z-score to an EMA, this indicator provides traders with insights into the strength and momentum of price movements relative to a smoothed average. This enables better detection of overbought and oversold conditions, as well as potential trend reversals.
The use of the Z-score helps filter out noise and provides more robust signals by highlighting extreme deviations from the mean, allowing traders to make more informed decisions in both trending and ranging markets.
Technical Composition and Calculation
The "Ema Z-score" script consists of two main components: the Exponential Moving Average (EMA) and the Z-score calculation. The EMA is calculated over a user-defined length, smoothing price movements to provide a clearer trend line. The Z-score is then derived by measuring the deviation of the current EMA value from the mean of the EMA over a lookback period, divided by the standard deviation of the EMA during that same period.
For the Z-score calculation, the script first computes the mean EMA over the lookback period using the ta.ema function. It then calculates the standard deviation of the EMA over the same period using the ta.stdev function. The Z-score is determined by subtracting the mean EMA from the current EMA value and dividing by the standard deviation, producing a normalized measure of deviation from the average.
Features and User Inputs
The "Ema Z-score" script offers several customizable inputs that allow traders to adjust the indicator according to their strategies. The EMA Length controls the smoothing period of the EMA, while the Lookback Period defines how far back the script looks when calculating the mean and standard deviation for the Z-score. Customizable thresholds allow traders to define when the Z-score signals potential uptrends or downtrends, based on their chosen levels of deviation.
Practical Applications
The "Ema Z-score" indicator is designed for traders who want to better understand price deviations from the mean and use those insights to identify potential trading opportunities. This tool is particularly effective for:
Identifying Overbought and Oversold Conditions: The Z-score provides a quantitative measure of how far the price has deviated from the mean, helping traders spot extreme conditions that could lead to reversals. Detecting Trend Reversals: By monitoring when the Z-score crosses certain thresholds, traders can identify potential trend reversals early and adjust their positions accordingly. Confirming Trend Strength: The Z-score can help confirm whether a price move is backed by momentum or is likely to revert to the mean, providing additional context for trade entries and exits.
Advantages and Strategic Value
The "Ema Z-score" script offers a significant advantage by combining the smoothing effect of the EMA with the precision of Z-score analysis. This approach reduces the impact of market noise while highlighting meaningful deviations from the norm. The ability to quantify deviations in terms of standard deviations gives traders a statistical edge in identifying overbought or oversold conditions and potential trend shifts. This makes the "Ema Z-score" an effective tool for both trend-following and contrarian strategies.
Alerts and Visual Cues
The script includes alert conditions to notify traders of key Z-score threshold crossings. The "Ema Z-score Long" alert is triggered when the Z-score exceeds the upper threshold, signaling a potential upward trend. Conversely, the "Ema Z-score Short" alert signals a possible downward trend when the Z-score falls below the lower threshold. Visual cues such as color changes in the bar chart and Z-score plot help traders easily identify these conditions on the chart.
Summary and Usage Tips
The "Ema Z-score | viResearch" indicator offers a unique combination of EMA smoothing and Z-score analysis, giving traders a statistical measure of price deviations and improving their ability to detect overbought or oversold conditions, trend reversals, and trend confirmations. By incorporating this script into your trading strategy, you can better quantify price extremes and make more informed decisions in both volatile and stable markets. Whether you're focused on spotting early reversals or confirming ongoing trends, the "Ema Z-score" provides a reliable and customizable solution.
Note: Backtests are based on past results and are not indicative of future performance.
Forex Macro Metrics [MacroGlide]"Forex Macro Metrics " is a powerful tool for analyzing macroeconomic metrics, designed to help traders make more informed decisions in the forex market. This indicator displays key economic indicators such as interest rates, money supply (M1 and M2), unemployment rate, and government debt for various currencies and their pairs, allowing users to assess the macroeconomic differences between the base and quote currencies.
Key Features:
• Interest Rates Display: Includes interest rates for major world currencies with the ability to show the differential between the base and quote currencies.
• Money Supply Analysis (M1 and M2): Displays the money supply for both the base and quote currencies, including differential calculations.
• Unemployment Rate: Compares the unemployment rates between currencies, showing the differences on the chart.
• Government Debt: Shows government debt levels for the base and quote currencies with differential calculations.
• Customizable Options: Enable/disable specific metrics and adjust colors for better visual clarity.
How to Use:
• Select a Currency Pair: Apply the indicator to your chart and choose the desired currency pair. The indicator will automatically load the relevant data for the base and quote currencies.
• Adjust Display Settings: Use the indicator settings to enable or disable specific metrics and their differentials.
• Analyze the Data: Compare the economic conditions of the two currencies through the charts and identify potential trading opportunities based on macroeconomic differences.
Methodology:
The indicator uses economic data available through TradingView tickers to calculate the values of the base and quote currencies. Differentials are calculated by subtracting the values of the quote currency from the base currency, allowing for a visual assessment of their differences. The displayed data includes historical changes, helping to identify trends and potential reversal points.
Originality and Usefulness:
"Forex Macro Metrics " is a unique tool that combines several key macroeconomic indicators into one comprehensive indicator. This simplifies the analysis process for traders looking to understand the fundamental differences between currencies. Using this approach provides an advantage in assessing long-term trends and potential shifts in currency pairs driven by changes in macroeconomic conditions.
Charts:
The indicator displays data in the form of lines and areas on the chart, with interest rates shown as lines for the base and quote currencies, accompanied by an area representing the differential. For money supply (M1 and M2), lines are drawn for each currency, with areas highlighting the differences. Similarly, the unemployment rate and government debt are displayed with clear visual separation of the data and their differentials, making it easy to compare and analyze the macroeconomic conditions of the currencies involved.
Enjoy the game!
Kalman PSaR [BackQuant]Kalman PSaR
Overview and Innovation
The Kalman PSaR combines the well-known Parabolic SAR (PSaR) with the advanced smoothing capabilities of the Kalman Filter . This innovative tool aims to enhance the traditional PSaR by integrating Kalman filtering, which reduces noise and improves trend detection. The Kalman PSaR adapts dynamically to price movements, making it a highly effective indicator for spotting trend shifts while minimizing the impact of false signals caused by market volatility.
Please Find the Basic Kalman Here:
Kalman Filter Dynamics
The Kalman Filter is a powerful algorithm for estimating the true value of a system amidst noisy data. In the Kalman PSaR, this filter is applied to the high, low, and closing prices, resulting in a smoother and more accurate representation of price action. The filter’s parameters—process noise and measurement noise—are customizable, allowing traders to fine-tune the sensitivity of the indicator to market conditions. By reducing the impact of noise, the Kalman-filtered PSaR offers clearer signals for identifying trend reversals and continuations.
Enhanced PSaR Calculation
The traditional Parabolic SAR is a popular trend-following indicator that highlights potential entry and exit points based on price acceleration. In the Kalman PSaR, this calculation is enhanced by the Kalman-filtered prices, providing a smoother and more reliable signal. The indicator continuously updates based on the acceleration factor and max step values, while the Kalman filter ensures that sudden price spikes or market noise do not trigger false signals.
Min Step and Max Step: These settings control the sensitivity of the PSaR. The Min Step sets the initial acceleration factor, while the Max Step limits how fast the PSaR adapts to price changes, helping traders fine-tune the indicator’s responsiveness.
Optional Smoothing Techniques To further enhance the signal clarity, the Kalman PSaR includes an optional smoothing feature. Traders can choose from various smoothing methods, such as SMA, Hull, EMA, WMA, TEMA, and more, to reduce short-term fluctuations and emphasize the underlying trend. The smoothing period is customizable, allowing traders to adjust the indicator’s behavior according to their preferred trading style and timeframe.
Color-Coded Candle Painting The Kalman PSaR features color-coded candles that change according to the trend direction. When the price is above the PSaR, candles are painted green to indicate a long trend, and when the price is below the PSaR, candles are painted red to signal a short trend. This visual representation makes it easy to interpret market sentiment at a glance, improving decision-making speed during fast-moving markets.
Key Features and Customization
Kalman Filter Customization: The process noise and measurement noise parameters allow traders to adjust how aggressively the filter adapts to price changes, making it suitable for both volatile and stable markets.
Smoothing Options: A variety of moving average types, such as SMA, Hull, EMA, and more, can be applied to smooth the PSaR values, ensuring that the signal remains clear even in choppy markets.
Dynamic Trend Detection: The Kalman PSaR dynamically updates based on price movements, helping traders spot trend reversals early while filtering out false signals caused by short-term volatility.
Bar Coloring and PSaR Plotting: Traders can choose to color candles based on trend direction or plot the PSaR directly on the chart for additional visual clarity.
Practical Applications
Trend-Following Strategies: The Kalman PSaR excels in trend-following strategies by providing timely signals of trend changes. The dynamic nature of the indicator allows traders to capture significant price movements while avoiding market noise.
Reversal Identification: The indicator’s ability to filter out noise and provide smoother signals makes it ideal for identifying reversals in volatile markets.
Risk Management: By plotting clear stop levels based on the PSaR, traders can use this indicator to effectively manage risk, placing stop-loss orders at key points based on the trend direction.
Conclusion
The Kalman PSaR is a fusion of the classic Parabolic SAR and the Kalman filter, offering enhanced trend detection with reduced noise. Its customizable filtering and smoothing options, combined with dynamic trend-following capabilities, make it a versatile tool for traders seeking to improve their timing and signal accuracy. The adaptive nature of the Kalman filter, combined with the robust PSaR logic, helps traders stay on the right side of the market and manage risk more effectively.
Fisher Divergence Overlay [BackQuant]Fisher Divergence Overlay
You can find the other Fisher Script Here !
Overlay Adaptation The Fisher Divergence Overlay is a newly enhanced version of the original Fisher Transformation indicator, designed specifically to be plotted directly on price charts. This adaptation allows traders to visualize Fisher Transform signals, divergences, and trend shifts directly over the price action, offering a more intuitive way to monitor market trends and potential reversals without the need for separate indicator windows. The overlay structure is particularly useful for spotting divergences and shifts in momentum as they relate to key price levels.
Why Turn the Fisher into an Overlay?
By transforming the Fisher Divergence indicator into an overlay, traders gain a more direct view of the relationship between price movements and the Fisher Transformation's signals. Divergences and midline crossovers, key components of the Fisher strategy, can now be clearly seen relative to the current price action. The decision to integrate this functionality as an overlay allows for a cleaner and more insightful trading experience, helping traders make quicker, more informed decisions based on market dynamics.
Midline Cross Signals : The overlay makes it easy to see when the Fisher Transform crosses above or below the midline, a critical signal for potential trend reversals.
Divergence Signals : Both regular and hidden divergences are plotted directly over price bars, offering immediate visual confirmation of potential trend shifts.
Key Features of the Overlay Version
Kaufman Adaptive Moving Average (KAMA): The Fisher Transformation in this overlay version can be adapted using Kaufman’s Adaptive Moving Average (KAMA). This enhances the Fisher's responsiveness to current market volatility, smoothing out price data while maintaining the accuracy of trend signals.
Divergence Detection: The overlay includes both regular and hidden bullish and bearish divergence detection, with these divergences plotted directly on the price chart. This visual feedback makes it easier for traders to spot when the momentum of the Fisher Transform deviates from the actual price movement, often signaling potential reversals.
Dynamic Bar Coloring: The bars are color-coded based on either the Fisher trend or divergences, allowing traders to visually interpret market sentiment without additional analysis. Green bars signal an upward trend or bullish divergence, while red bars indicate a downward trend or bearish divergence.
Take Profit Hues: In conjunction with a normalized RSI, the overlay includes background hues for overbought and oversold conditions, providing additional context for exit points or potential reversals.
How to Use the Fisher Overlay Traders can use this overlay to streamline their workflow by having both the Fisher signals and price action in the same visual space. The key signals include:
Midline Cross Signals: A crossover of the Fisher Transform above the midline often indicates a shift toward bullish momentum, while a cross below suggests bearish momentum.
Divergences: Regular and hidden divergences, displayed directly on the chart, help traders identify moments when the momentum of the Fisher Transform is in contrast with price movements, signaling potential reversals.
RSI Confluence: Overbought and oversold signals, provided by the integrated RSI, give further insight into potential exhaustion points in the market, marked by background color changes on the chart.
Strategic Value of the Fisher Divergence Overlay
This overlay offers a streamlined, efficient way to interpret Fisher Transform signals, divergences, and confluence signals like RSI in real-time. The visual integration of these signals with price action enhances decision-making by providing immediate context, making it easier to spot high-probability trade setups.
Trend Confirmation: The overlay version helps confirm trends by visually aligning Fisher Transform signals with price levels. Traders can use this feature to strengthen their conviction before entering or exiting a trade.
Adaptability: With the option to use KAMA for adaptive price smoothing, this overlay remains responsive across different market environments, making it suitable for both trending and volatile markets.
Summary and Interpretation Tips
It enhances the traditional Fisher Transform with visual elements like divergence detection, RSI confluence, and midline cross signals. By overlaying these elements directly on the price chart, traders can quickly interpret key signals and make better trading decisions.
Use this indicator to identify trend shifts and potential reversals by focusing on midline crossovers and divergences. The visual cues—bar colors, divergence labels, and background hues—make it easy to spot actionable moments without cluttering the chart. For best results, combine this overlay with other trend-following tools to confirm your trades and maximize the utility of Fisher Transform signals.
Multi-Kernel CCI [BackQuant]Multi-Kernel CCI
Conceptual Foundation and Innovation
It offers a fresh take on the Commodity Channel Index (CCI) by integrating three distinct kernel functions—Exponential Decay, Gaussian Decay, and Cosine Decay—to create a more robust and adaptive momentum indicator. The use of these kernel functions allows the CCI calculation to be more responsive to price changes while smoothing out noise, providing traders with clearer trend signals and reducing false alerts in varying market conditions.
Technical Composition and Calculation
The core of this indicator is a multi-kernel approach to calculating the CCI, where three different decay kernels are applied to the price source. Each kernel provides a unique weighting mechanism for price data over a user-defined lookback period. The result is an average of these three kernel calculations, which serves as the foundation for the CCI calculation. This innovative approach makes the Multi-Kernel CCI more adaptive to different market conditions compared to traditional CCI calculations.
Exponential Decay Kernel: Applies an exponential weighting to recent price data, giving more importance to recent values while smoothing out older data.
Gaussian Decay Kernel: Weights data using a Gaussian function, ensuring smooth transitions between price points and reducing outliers' impact.
Cosine Decay Kernel: Utilizes a cosine function to apply a unique oscillating weight to the data, capturing cyclical market movements more effectively.
Adaptive Thresholding: Like the Adaptive Momentum Oscillator, this indicator adjusts its long and short thresholds dynamically using percentile-based calculations over historical CCI values.
Features and User Inputs The Multi-Kernel CCI offers a wide range of customization options for traders:
Kernel Calculation Length & Alpha: Traders can fine-tune the sensitivity of the CCI by adjusting the length of the kernel calculation and the alpha parameter for the Exponential Decay Kernel.
Adaptive Thresholds: The indicator provides percentile-based thresholds for both long and short signals, allowing traders to dynamically adjust their signals based on historical data.
Extreme Value Detection: This feature highlights extreme overbought and oversold conditions with customizable thresholds and background hues, visually aiding in identifying high-probability reversal zones.
Divergence Detection: The script includes a divergence detection feature, identifying regular and hidden bullish or bearish divergences to help traders spot potential trend reversals.
Practical Applications The Multi-Kernel CCI excels in markets where adaptive trend detection and momentum confirmation are critical. Traders can leverage this tool in several ways:
Adaptive Trend Following: The dynamically adjusting thresholds allow traders to capture trends more effectively while avoiding false signals during consolidations or choppy markets.
Reversal Detection: The multi-kernel approach ensures that reversals are detected with greater precision, particularly in volatile markets where traditional indicators might fail.
Divergence Identification: With built-in divergence detection, the indicator provides traders with an early warning of potential trend reversals, helping to time their entries and exits more effectively.
Advantages and Strategic Value The Multi-Kernel CCI offers several strategic advantages over traditional CCI indicators:
Multi-Kernel Smoothing:
By using multiple decay kernels, this CCI calculation is better suited to detect subtle changes in market momentum, reducing the impact of noise and providing clearer trend signals.
Dynamic Thresholds:
The adaptive percentile-based thresholds ensure that the indicator remains relevant across different market conditions, enhancing signal accuracy.
Visual and Analytical Aids:
With features like extreme value detection and divergence spotting, this indicator equips traders with powerful tools to confirm trend strength and identify potential reversals.
Summary and Usage Tips
The Multi-Kernel CCI is a highly versatile tool for traders seeking a more adaptive and robust momentum indicator. Its multi-kernel foundation provides smoother, more reliable signals, while the adaptive thresholds and divergence detection features help traders refine their entries and exits. The dynamic nature of this indicator makes it ideal for both trend-following and reversal strategies in volatile markets.
Traders should experiment with the kernel calculation length and alpha parameter to align the indicator's sensitivity with their specific trading style and market conditions. Additionally, the adaptive thresholds can be fine-tuned to ensure the CCI captures the most significant trend changes without being overly reactive to short-term fluctuations.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Kalman Filter RoC with Adaptive Thresholds [BackQuant]Kalman Filter RoC with Adaptive Thresholds
Another Kalman Script !!
Please Find the Basic Kalman Here:
Overview and Purpose
The Kalman Filter RoC with Adaptive Thresholds is an advanced tool designed for traders seeking to refine their trend detection and momentum analysis. By combining the robustness of the Kalman filter with the Rate of Change (RoC) indicator, this tool offers a highly responsive and adaptive method to identify shifts in market trends. The inclusion of adaptive thresholding further enhances the indicator’s precision by dynamically adjusting to market volatility, providing traders with reliable entry and exit signals.
Kalman Filter Dynamics
The Kalman Filter is renowned for its ability to estimate the true state of a system amidst noisy data. In this indicator, the Kalman filter is applied to the price data to smooth out fluctuations and generate a more accurate representation of the underlying trend. This is particularly useful in volatile markets where noise can obscure the true direction of price movements. The Kalman filter adapts in real-time based on user-defined parameters, such as process noise and measurement noise, making it highly customizable for different market conditions.
Rate of Change (RoC) and Smoothing The Rate of Change (RoC) is a classic momentum indicator that measures the percentage change in price over a specific period. By integrating it with the Kalman-filtered price, the RoC becomes more responsive to genuine price trends while filtering out short-term noise. An optional smoothing feature using the ALMA (Arnaud Legoux Moving Average) further refines the signal, allowing traders to adjust the calculation length and smoothing factor (sigma) for even greater precision.
Adaptive Thresholds A key innovation in this indicator is the adaptive thresholding mechanism. Traditional RoC indicators rely on static thresholds to identify overbought or oversold conditions, but the Kalman Filter RoC adapts these thresholds dynamically. The adaptive thresholds are calculated based on the historical volatility of the filtered RoC values, allowing the indicator to adjust in response to changing market conditions. This feature reduces the risk of false signals in choppy or highly volatile markets.
Divergence Detection The Kalman Filter RoC also includes divergence detection, helping traders identify when the momentum of the RoC diverges from the price action. Divergences can often signal potential reversals or trend continuations, making them a valuable tool in any trader’s toolkit. Regular and hidden divergences are plotted directly on the chart, providing visual cues for traders to act upon.
Customization and Flexibility This indicator offers a wide range of customization options, making it suitable for various trading strategies and market conditions:
Process Noise & Measurement Noise: These parameters control how sensitive the Kalman filter is to price changes and help traders fine-tune the balance between noise reduction and signal responsiveness.
ALMA Smoothing: Traders can apply ALMA smoothing to the RoC signal to reduce short-term volatility and improve signal clarity.
Adaptive Threshold Calculation Period: The length of the lookback period for the adaptive thresholds can be adjusted, allowing traders to tailor the indicator to fit their specific trading style.
Practical Applications
Trend Detection: The Kalman-filtered RoC helps identify shifts in momentum, making it easier for traders to spot emerging trends early. The dynamic thresholding ensures that these signals are reliable, even in volatile markets.
Divergence Trading: Divergences between the RoC and price action are clear indicators of potential trend reversals. The visual plotting of divergences simplifies the process of identifying these opportunities.
Momentum Analysis: The combination of Kalman filtering and RoC provides a smoother, more accurate view of market momentum, helping traders stay on the right side of the market.
Conclusion
The Kalman Filter RoC is a powerful and adaptable tool that merges advanced filtering techniques with momentum analysis. Its real-time responsiveness and dynamic thresholding make it a highly effective indicator for identifying trends, managing risk, and capitalizing on divergence signals. Traders looking to enhance their trend-following or momentum strategies will find this indicator to be a valuable addition to their toolkit.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Adaptive Momentum Oscillator [BackQuant]Adaptive Momentum Oscillator
Please take time to read the following.
Conceptual Foundation and Innovation
The Adaptive Momentum Oscillator brings a new approach to momentum trading by introducing percentile-based adaptive thresholding. Unlike traditional momentum oscillators that rely on static overbought and oversold levels, this indicator adjusts dynamically to changing market conditions, providing more relevant signals in real-time. By combining percentile-based thresholds with a smoothed momentum oscillator, this tool allows traders to detect trend shifts with a higher degree of accuracy.
Technical Composition and Calculation
The core of this oscillator uses a lookback period to calculate the highest and lowest values of a smoothed price source (using a non-robust moving average). These values are then used to compute the oscillator, which normalizes the current price between the lookback high and low. The true innovation lies in its adaptive thresholds, which adjust based on percentiles of past oscillator values over a user-defined lookback period.
Lookback Period: The indicator checks the highest and lowest smoothed price over a set period, which becomes the basis for calculating momentum.
Percentile-Based Thresholds: The upper and lower thresholds are dynamically set at user-defined percentiles of historical momentum values, allowing the oscillator to adapt to the volatility and strength of the market.
Smoothing Length: Users can adjust the smoothing of the source input to fine-tune the sensitivity of the oscillator.
Features and User Inputs offer a host of customizable settings to suit different market conditions and trading strategies:
Adaptive Thresholding: Traders can set the lookback period and define the percentile levels for the upper (long) and lower (short) thresholds. This provides the ability to dynamically adjust to changing market conditions and avoid static thresholds that may become irrelevant over time.
Signal Line Customization: Users can configure the signal line width, colors for long, short, and neutral conditions, and choose whether to display adaptive threshold lines on the chart.
Candle Coloring: An optional feature allows traders to color the price bars based on the oscillator's trend signal, adding a visual confirmation layer for trend shifts.
Practical Applications
This oscillator is particularly effective in markets where the strength and direction of momentum are essential for identifying potential trend reversals or confirming ongoing trends. Traders can leverage the Adaptive Momentum Oscillator to:
Capture Adaptive Trends: The percentile-based thresholds adjust dynamically, ensuring that traders catch significant trends while filtering out market noise.
Avoid False Signals: By adapting to historical momentum levels, the oscillator reduces the risk of false breakouts or breakdowns, allowing for more reliable entries and exits.
Optimize Entries and Exits: With dynamically adjusting thresholds, the oscillator helps traders time their positions more effectively, minimizing the risk of getting caught in choppy or uncertain markets.
Advantages and Strategic Value
It offers a clear advantage over traditional static oscillators by continuously adjusting its sensitivity to market conditions. The adaptive percentile thresholds ensure that the indicator remains relevant, regardless of changes in volatility or market direction. This feature, combined with a customizable UI, makes the Adaptive Momentum Oscillator a powerful tool for traders looking to refine their momentum-based strategies with dynamic thresholds.
Summary and Usage Tips
The Adaptive Momentum Oscillator is a versatile tool for both trend-following and contrarian traders. Its dynamic nature allows for better alignment with current market conditions, while its user-friendly inputs offer extensive customization options. Traders are encouraged to experiment with the percentile-based threshold settings to find the optimal balance between signal sensitivity and noise reduction, particularly in fast-moving or volatile markets.
This indicator is best used in combination with other trend-confirmation tools, offering a dynamic layer to your trading system.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Price-Volume w Trendline - Strategy [presentTrading]█ Introduction and How it is Different
The Price-Volume with Trendline Strategy is an innovative strategy that combines volume profile analysis, price-based Z-scores, and dynamic trendline filtering to identify optimal entry and exit points in the market. What sets this strategy apart is the integration of volume concentration (Point of Control or PoC) with dynamic volatility thresholds. Additionally, this strategy introduces a multi-step take profit (TP) mechanism that adjusts based on predefined levels, allowing traders to exit trades progressively while capitalizing on market momentum.
BTCUSD 6hr LS Performance
█ Strategy, How it Works: Detailed Explanation
The combination of multiple indicators and methodologies serves to create a more robust and reliable trading system. Each element is carefully chosen for its complementary role in providing accurate signals while minimizing false entries and exits. Here’s why the different components were chosen and how they work together:
- PoC and Z-Scores: The volume profile identifies key price areas, while the Z-score measures deviations from the mean. Together, they highlight points where the market is likely to react. For example, when the Z-score indicates an oversold condition near a PoC support level, it increases the probability of a reversal, providing a clear entry signal.
- Trendlines and Z-Scores: Trendlines serve as a secondary filter to ensure that price deviations identified by Z-scores align with broader market trends. This ensures that trades are only entered when the price has both deviated from its average and broken through a significant trendline level, reducing the likelihood of false signals.
- Multi-Step TP and Risk Management: Finally, the multi-step take profit logic works in tandem with the entry signals generated by the PoC, Z-scores, and trendlines. As the price moves in favor of the trade, profits are gradually locked in, ensuring the trader captures gains while still leaving room for further upside.
🔶 Point of Control (PoC) and Volume Profile Analysis
The PoC identifies the price level with the highest volume concentration within a specified lookback period. This price level represents where the most trading activity has occurred, often acting as a strong support or resistance. By breaking down the range into several rows (bins), the strategy identifies how much volume was traded at each price level.
🔶 Z-Score Calculation
The Z-score is a statistical metric that measures how far the current price is from its mean, expressed in terms of standard deviations. This is calculated both for price deviation and PoC-based deviation.
🔶 Trendline Breakout Filtering
The trendline filtering is a crucial aspect that refines entry signals by confirming trend continuation or reversals. It calculates trendlines based on pivot highs and lows using the selected method (e.g., ATR or standard deviation).
🔶 Multi-Step Take Profit
The multi-step take profit mechanism allows the strategy to take partial profits at several predefined levels. For example, when the price reaches 3%, 8%, 14%, or 21% above (or below) the entry price, it exits portions of the position. This is a useful technique for locking in profits as the market moves favorably.
Local
█ Usage
The Price-Volume with Trendline Strategy can be applied to various asset classes, including stocks, cryptocurrencies, and commodities. It is particularly effective in volatile markets where price deviations and volume concentrations signal potential reversals or trend continuations. By adjusting the settings for volatility and the lookback period, this strategy can be tailored to both short-term intraday trades and longer-term swing trades.
█ Default Settings
The default settings in the strategy play a vital role in shaping its performance.
- POC_lookbackLength (144): This defines the number of bars used to calculate the PoC. A longer lookback captures more data, leading to a more stable PoC, but may result in delayed signals. A shorter lookback increases responsiveness but may introduce noise.
- priceDeviationLength (200): This determines the period for calculating the standard deviation of price. A higher length smooths out the volatility, reducing the likelihood of false signals. Shorter lengths make the strategy more sensitive to sudden price movements.
- TL_length (14): Controls the swing detection period for trendline calculation. A shorter length will generate more frequent trendline breakouts, while a longer length captures only significant moves.
- Stop Loss and Take Profit: The strategy offers both fixed and SuperTrend-based stop losses. SuperTrend is adaptive to volatility, while fixed stop losses provide simpler risk control. The multi-step take profit ensures that profits are secured progressively, which can improve performance in trending markets by reducing the risk of full reversals.
Each of these settings can significantly affect the strategy’s risk-reward balance. For instance, increasing the stop loss level or the take profit percentages allows the strategy to stay in trades longer, potentially increasing profit per trade but at the cost of larger drawdowns. Conversely, tighter stops and smaller profit targets result in more frequent trades with lower average profit per trade.
Black-Scholes option price model & delta hedge strategyBlack-Scholes Option Pricing Model Strategy
The strategy is based on the Black-Scholes option pricing model and allows the calculation of option prices, various option metrics (the Greeks), and the creation of synthetic positions through delta hedging.
ATTENTION!
Trading derivative financial instruments involves high risks. The author of the strategy is not responsible for your financial results! The strategy is not self-sufficient for generating profit! It is created exclusively for constructing a synthetic derivative financial instrument. Also, there might be errors in the script, so use it at your own risk! I would appreciate it if you point out any mistakes in the comments! I would be even more grateful if you send the corrected code!
Application Scope
This strategy can be used for delta hedging short positions in sold options. For example, suppose you sold a call option on Bitcoin on the Deribit exchange with a strike price of $60,000 and an expiration date of September 27, 2024. Using this script, you can create a delta hedge to protect against the risk of loss in the option position if the price of Bitcoin rises.
Another example: Suppose you use staking of altcoins in your strategies, for which options are not available. By using this strategy, you can hedge the risk of a price drop (Put option). In this case, you won't lose money if the underlying asset price increases, unlike with a short futures position.
Another example: You received an airdrop, but your tokens will not be fully unlocked soon. Using this script, you can fully hedge your position and preserve their dollar value by the time the tokens are fully unlocked. And you won't fear the underlying asset price increasing, as the loss in the event of a price rise is limited to the option premium you will pay if you rebalance the portfolio.
Of course, this script can also be used for simple directional trading of momentum and mean reversion strategies!
Key Features and Input Parameters
1. Option settings:
- Style of option: "European vanilla", "Binary", "Asian geometric".
- Type of option: "Call" (bet on the rise) or "Put" (bet on the fall).
- Strike price: the option contract price.
- Expiration: the expiry date and time of the option contract.
2. Market statistic settings:
- Type of price source: open, high, low, close, hl2, hlc3, ohlc4, hlcc4 (using hl2, hlc3, ohlc4, hlcc4 allows smoothing the price in more volatile series).
- Risk-free return symbol: the risk-free rate for the market where the underlying asset is traded. For the cryptocurrency market, the return on the funding rate arbitrage strategy is accepted (a special function is written for its calculation based on the Premium Price).
- Volatility calculation model: realized (standard deviation over a moving period), implied (e.g., DVOL or VIX), or custom (you can specify a specific number in the field below). For the cryptocurrency market, the calculation of implied volatility is implemented based on the product of the realized volatility ratio of the considered asset and Bitcoin to the Bitcoin implied volatility index.
- User implied volatility: fixed implied volatility (used if "Custom" is selected in the "Volatility Calculation Method").
3. Display settings:
- Choose metric: what to display on the indicator scale – the price of the underlying asset, the option price, volatility, or Greeks (all are available).
- Measure: bps (basis points), percent. This parameter allows choosing the unit of measurement for the displayed metric (for all except the Greeks).
4. Trading settings:
- Hedge model: None (do not trade, default), Simple (just open a position for the full volume when the strike price is crossed), Synthetic option (creating a synthetic option based on the Black-Scholes model).
- Position side: Long, Short.
- Position size: the number of units of the underlying asset needed to create the option.
- Strategy start time: the moment in time after which the strategy will start working to create a synthetic option.
- Delta hedge interval: the interval in minutes for rebalancing the portfolio. For example, a value of 5 corresponds to rebalancing the portfolio every 5 minutes.
Post scriptum
My strategy based on the SegaRKO model. Many thanks to the author! Unfortunately, I don't have enough reputation points to include a link to the author in the description. You can find the original model via the link in the code, as well as through the search indicators on the charts by entering the name: "Black-Scholes Option Pricing Model". I have significantly improved the model: the calculation of volatility, risk-free rate and time value of the option have been reworked. The code performance has also been significantly optimized. And the most significant change is the execution, with which you can now trade using this script.
TradeTracker v33 - Interactive Journal [AR33_]TradeTracker v33 - Interactive Journal is a unique tool designed to enhance your trading experience by integrating an interactive journal directly onto your charts. Unlike traditional trading journals that require manual entries outside of TradingView, this script allows traders to document, track, and review their trades in real-time, right where the action happens.
What sets TradeTracker v33 apart from existing tools is its seamless blend of note-taking, task management, and performance tracking—all within a single, intuitive interface. With features like customizable checklists, due dates, and color-coded status indicators, this script provides a powerful and practical solution for traders who want to stay organized and disciplined.
2. Description
. TradeTracker v33 - Interactive Journal is designed to keep traders on track by allowing them to record trade-related notes, set tasks, and mark progress directly on their charts.
Here’s how it works:
• Purpose: The script serves as an all-in-one journal and task manager, helping traders document their trading strategies, track ongoing tasks, and review completed actions. It’s particularly useful for maintaining discipline and ensuring that every trade is executed according to a well-thought-out plan.
• How It Works:
• Interactive Notes and Tasks: Users can create and manage notes and tasks directly on their charts. Each note can be customized with a title, description, due date, and completion status.
• Status Indicators: Tasks are color-coded based on their status—green for completed, red for overdue, and default colors for pending tasks—allowing traders to quickly assess their progress.
• Dynamic Display: Notes are displayed in a clean, organized table on the chart, making it easy to review multiple tasks without cluttering the trading interface.
• Usage:
• Adding Notes: Simply fill in the note title, content, and optional due date within the script’s input settings, and the note will appear on your chart.
• Tracking Progress: Mark tasks as completed with a simple toggle, and the script will update their status in real-time.
• Customizing Your Workflow: Adjust the position, size, and visibility of notes to fit your trading style, ensuring that your journal supports rather than distracts from your trading activities.
3. Chart Presentation
To provide a clear and focused user experience, TradeTracker v33 - Interactive Journal is designed to be the sole feature on your chart when published. This ensures that users can easily identify and interact with their notes and tasks without any unnecessary distractions.
• Clean and Focused Display: The chart will exclusively display the interactive journal, showcasing how tasks and notes appear and update in real-time as you manage them.
• Useful Annotations: Annotations such as checkboxes and status indicators are clearly explained within the script’s description and are vital to understanding the functionality of the tool.
• Minimal Distractions: Only elements directly related to the script’s functionality are included on the chart, ensuring that users can easily follow along and implement the script in their own trading setup.
Enhanced Local Polynomial Regression [Yosiet]Local Polynomial Regression (LPR) is an advanced statistical method that offers a flexible approach to estimating the underlying trend in financial time series data.
The Mathematical Explanation
The core idea of LPR is to fit a polynomial of degree p at each point x using weighted least squares. The weight of each data point decreases with its distance from x, controlled by a kernel function and a bandwidth parameter.
The general form of the local polynomial estimator is:
β̂(x) = argmin Σ K((Xi - x) / h) (Yi - β0 - β1(Xi - x) - ... - βp(Xi - x)^p)^2
Where:
β̂(x) is the vector of estimated coefficients
K is the kernel function
h is the bandwidth
Xi and Yi are the predictor and response variables
p is the degree of the polynomial
Our implementation uses the Epanechnikov kernel:
K(u) = 3/4 * (1 - u^2) for |u| ≤ 1, 0 otherwise
The Implementation
This script implements LPR for the easier way to interpret its values with the following key components:
Input Parameters: Can adjust the lookback period, bandwidth, and polynomial degree.
Kernel Function: The Epanechnikov kernel is used for weighting.
LPR Function: Implements the core algorithm using matrix operations.
Signal Generation: Generates buy/sell signals based on crossovers of smoothed price and LPR results.
How to Use
Apply the indicator to your chart in TradingView.
Adjust the input parameters:
Lookback Period: Controls how many past bars are considered.
Bandwidth: Affects the smoothness of the regression line.
Polynomial Degree: Determines the complexity of the local fit.
Signal Smoothing Length: Adjusts the responsiveness of buy/sell signals.
Monitor buy/sell signals for potential trade entries.
Limitations
Sensitivity to Parameters: The choice of bandwidth and polynomial degree significantly impacts the results.
Lag: Like all trend-following indicators, LPR may lag behind rapid price movements.
Edge Effects: The indicator may be less reliable at the edges of the data (recent bars).
Recommendations
Parameter Optimization: Experiment with different lookback periods, bandwidths, and polynomial degrees to find the best fit for your trading style and timeframe.
Combine with Other Indicators: Use LPR in conjunction with momentum oscillators or volume indicators for confirmation.
Multiple Timeframes: Apply LPR on different timeframes to gain a more comprehensive view of the trend.
Avoid Overfitting: Be cautious of using high polynomial degrees, as they may lead to overfitting on historical data.
Consider Market Conditions: LPR works best in trending markets; be aware of its limitations in ranging or highly volatile conditions.
Backtest Thoroughly: Always backtest strategies based on LPR across different market conditions before live trading.
Conclusion
Local Polynomial Regression offers a sophisticated approach to trend analysis in financial markets. By providing a flexible, adaptive trend line, it can help traders identify potential entry and exit points with greater precision than traditional moving averages. However, like all technical indicators, it should be used as part of a comprehensive trading strategy that includes proper risk management and consideration of fundamental factors.
if you have an strategy or idea and need to make it real through an indicator or trading bot, you can DM or comment
Honey Badger and Dip and Rip Days**Definitions**
A Honey Badger Day is defined as a day where the market dips below a certain threshold but then closes above it. Specifically:
- The day's low is less than or equal to the lower of either the opening price or the previous day's closing price.
- The day's closing price is greater than or equal to this same lower threshold.
Dip and Rip Day:A Dip and Rip Day is characterized by a more pronounced dip followed by a stronger recovery. The criteria are:
- The day's low is below 0.11% of the lower of the day's opening price or the previous day's closing price.
- The day's closing price is at least 0.405% higher than its opening price.
- The day's closing price is at least 0.792% higher than its low.
- The day's closing price is at least 0.405% higher than the previous day's closing price.
Both patterns indicate a day where the market experiences a dip but then recovers, with the Dip and Rip pattern showing a more dramatic movement in both directions.
DAILY CANDLE SIZE TRACKERDAILY CANDLE SIZE TRACKER
The daily candle size Indicator is a versatile tool designed to measure and analyze the length of candlesticks on your TradingView chart. This indicator helps traders gain insights into market volatility and price movement by displaying the relative size of particular size of candle in terms of its range (open to close) of particular day of week.
Key Features:
Visual Representation: Displays the length of every qualified candle directly on the chart, allowing for immediate visual assessment.
Historical Analysis: View the lengths of past candles to identify patterns or changes in market volatility over time of certain size of candle.
Usage:
• Probability analysis : It help to analyze candles based on candle size on particular days of the week.
• Volatility Assessment: Large candles may indicate higher market volatility, while smaller candles can suggest periods of consolidation or lower volatility.
• Trade Confirmation: Use candle length information in conjunction with other technical indicators to confirm trade signals and refine entry and exit points.
USER INPUTS
• DAY LABEL: Select Particular day of week or all days of week.
• START DATE : Select the prefer date from where you want to calculate.
• CANDLE LENGTH : Define length of candle on the basis of open to close.
******************* THIS INDICATOR ONLY WORKS ON DAILY TIMEFRAME**************
Screener | FractalystWhat’s the purpose of this indicator?
This indicator is part of the Optirange suite , which analyzes all timeframes using a mechanical top-down approach to determine the overall market bias. It helps you identify the specific timeframes and exact levels for positioning in longs, shorts, or guiding you on whether to stay away from trading a particular market condition.
The purpose of the Screener indicator is to track the contextual bias of multiple markets simultaneously on the charts without the need to switch between pairs. This allows traders to monitor various assets in real-time, enhancing decision-making efficiency and identifying potential trading opportunities more effectively.
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How does this indicator identify the overall market bias?
This indicator employs a systematic top-down approach, analyzing market structure, fractal blocks, and their mitigations from the 12M timeframe down to the 1D timeframe to uncover the story behind the market. This method helps identify the overall market bias, whether it’s bullish, bearish, or in consolidating conditions.
Below is a flowchart that illustrates the calculation behind the market context identification, demonstrating the systematic approach:
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According to the above trade plan, why do we only look for mitigations within Fractal Blocks of X1/X2?
In this context, "X" stands for a break in the market's structure, and the numbers (1 and 2) indicate the sequence of these breaks within the same trend direction, either up or down.
We focus on mitigations within Fractal Blocks during the X1/X2 stages because these points mark the early phase (X1) and the continuation (X2) of a trend. By doing so, we align our trades with the market's main direction and avoid getting stopped out in the middle of trends.
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How does this indicator identify ranges in a mechanical way?
Since the indicator is part of the Optirange suite , it follows the exact rules that Optirange utilizes to identify breaks of market structures in a mechanical manner.
Let’s take a closer look at how the ranges are calculated:
1- First, we need to understand the importance of following a set of mechanical rules in identifying market structure:
The image above illustrates the difference between a subjective and a mechanical approach to analyzing market structure. The subjective method often leads to uncertainty, where traders might struggle to pinpoint exact breaks in structure, resulting in inconsistent decision-making. Questions like “Is this a break?” or “Maybe this one...?” reflect the ambiguity of manual interpretation, which can cause confusion and errors in trading.
On the other hand, the mechanical approach depicted on the right side of the image follows a clear, rule-based method to define breaks in market structure. This systematic approach eliminates guesswork by providing precise criteria for identifying structural changes, such as marking structural invalidation levels where market bias shifts from bullish to bearish or vice versa. The mechanical method not only offers consistency but also integrates statistical probabilities , enhancing the trader's ability to make data-driven decisions.
By adhering to these mechanical rules, the Screener indicator ensures that ranges are identified consistently, allowing traders to rely on objective analysis rather than subjective interpretation . This approach is crucial for accurately defining market structures and making informed trading decisions.
2- Now let's take a look at a practical example of how the indicator utilizes Pivot points with a period of 2 to identify ranges:
In this image, we see a Bearish Scenario on the left and a Bullish Scenario on the right. The indicator starts by identifying the first significant swing on the chart. It then validates this swing by checking if there is a preceding swing high (for a bearish scenario) or swing low (for a bullish scenario). Once validated, the indicator confirms a break of structure when price closes below or above these points, respectively.
For instance, in the Bearish Scenario:
The first significant swing is identified.
The script checks for a preceding swing high before confirming any structural break.
A candle closure below the swing low confirms the first bearish break of structure.
This results in a confirmed market bias towards bearishness, with structural liquidity levels indicated for potential price targets.
In the Bullish Scenario:
The process is mirrored, identifying the first swing low and validating it with a preceding swing low.
A closure above this swing confirms the bullish break of structure.
This leads to a market bias towards bullishness, with invalidation levels to watch if the trend shifts.
This practical example demonstrates how the indicator systematically identifies market ranges, ensuring that traders can make informed decisions based on clear, rule-based criteria.
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How does this indicator identify ranges in a mechanical way, What are the underlying calculations?
Fractal blocks refer to the most extreme swing candle within the latest break. They can serve as significant levels for price rejection and may guide movements toward the next break, often in confluence with topdown analysis for added confirmation.
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What are mitigations, What are the underlying calculations?
Mitigations refer to specific price action occurrences identified by the script:
1- When the price reaches the most recent fractal block and confirms a swing candle, the script automatically draws a line from the swing to the fractal block bar and labels it with a checkmark.
2- If the price wicks through the invalidation level and then retraces back to the fractal block while forming a swing candle, the script labels this as a double mitigation on the chart.
This level will serve as the next potential invalidation level if a break occurs in the same direction.
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What does the right table display?
The table located at the right of your chart displays five colored symbols that represent the contextual market bias:
Green: The market is in a bullish condition.
Red: The market is in a bearish condition.
White: The market condition is uncertain, and it is advisable to stay away from trading.
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What does the bottom table display?
The bottom table can be turned on in the Optirange indicator and serves multiple purposes:
Range Counts and Mitigations: It shows the range counts and their mitigations across multiple timeframes, providing a comprehensive view of market dynamics.
Hourly Timeframe Probabilities: The bottom row of the bias table displays the probabilities for various hourly timeframes, helping to identify potential entry levels based on the multi-timeframe bias determined by the Screener.
In a bullish market context, you should look for long positions by focusing on hourly timeframes where buy-side probability exceeds 50%.
In a bearish market context, you should look for short positions by focusing on hourly timeframes where the sell-side probability exceeds 50%.
When the symbol is white within the Screener table, it signals that the market bias is unclear, and it's recommended to stay away from trading in such conditions.
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How the range probabilities are calculated?
Each break of market structure, denoted as X, is assigned a unique ID, starting from X1 for the first break, X2 for the second, and so on.
The probabilities are calculated based on breaks holding, meaning price closing through the liquidity level, rather than invalidation. This probability is then divided by the total count of similar numeric breaks.
For example, if 75 out of 100 bullish X1s become X2, then the probability of X1 becoming X2 on your charts will be displayed as 75% in the following format: ⬆ 75%
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What does the top table display?
The top table on the charts displays the current market context, offering insights into the underlying bias. It highlights the high-timeframe (HTF) bias and guides you on which timeframes you should use to enter long or short positions, based on the probability of success.
Additionally, when the market bias is unclear, the table clearly signals that it's best to avoid trading that specific market until the context or market story becomes clearer. This helps traders make informed decisions and avoid uncertain market conditions.
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How does the Screener indicator identify the market bias/context/story ?
- Market Structure: The Optirange indicator analyzes market structure across multiple timeframes, from a top-down perspective, including 12M, 6M, 3M, 1M, 2W, 1W, 3D, and 1D.
- Fractal Blocks: Once the market structure or current range is identified, the indicator automatically identifies the last push before the break and draws it as a box. These zones acts as a key area where the price often rejects from.
- Mitigations: After identifying the Fractal Block, the indicator checks for price mitigation or rejection within this zone. If mitigation occurs, meaning the price has reacted or rejected from the Fractal Block, the indicator draws a checkmark from the deepest candle within the Fractal Block to the initial candle that has created the zone.
- Bias Table: After identifying the three key elements—market structure, Fractal Blocks, and price mitigations—the indicator compiles this information into a multi-timeframe table. This table provides a comprehensive top-down perspective, showing what is happening from a structural standpoint across all timeframes. The Bias Table presents raw data, including identified Fractal Blocks and mitigations, to help traders understand the overall market trend. This data is crucial for the screener, which uses it to determine the current market bias based on a top-down analysis.
- Screener: Once all higher timeframes (HTF) and lower timeframes (LTF) are calculated using the indicator, it follows the exact rules outlined in the flowchart to determine the market bias. This systematic approach not only helps identify the current market trend but also suggests the exact timeframes to use for finding entry, particularly on hourly timeframes.
Example:
12M Timeframe:
OANDA:EURUSD
6M Timeframe :
OANDA:EURUSD
3M Timeframe :
OANDA:EURUSD
1M Timeframe :
OANDA:EURUSD
2W Timeframe :
OANDA:EURUSD
1W Timeframe :
OANDA:EURUSD
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User-input settings and customizations
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data. By utilizing our charting tools, the buyer acknowledges that neither the seller nor the creator assumes responsibility for decisions made using the information provided. The buyer assumes full responsibility and liability for any actions taken and their consequences, including potential financial losses. Therefore, by purchasing these charting tools, the customer acknowledges that neither the seller nor the creator is liable for any unfavorable outcomes resulting from the development, sale, or use of the products.
The buyer is responsible for canceling their subscription if they no longer wish to continue at the full retail price. Our policy does not include reimbursement, refunds, or chargebacks once the Terms and Conditions are accepted before purchase.
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Buy script for stocks mathematical calculation chart. it is totally based on the square root calculation of previous day + 66.66% of Square root. ( last dat sqrt+66.66% of Sqrt). buy above the value. best for stock in intraday
G.O.A.T. Scalper Diagnostics v1OVERVIEW:
The G.O.A.T. Scalper Diagnostics indicator system enables users to discover unorthodox indicator patterns, reading price charts in unusual ways, thus gaining an edge over the majority of market participants they trade against.
CONCEPTS:
Th G.O.A.T. Scalper Diagnostics is a system that aims to satisfy the fundamental condition for successful online trading - providing an edge.
It's a battle between advantages. To take other people's money, successful traders must have an advantage over everybody else. To hope for consistent success in trading, you need to do things differently and see what almost nobody else sees. Of course then you must act on it, and that's where the G.O.A.T. Scalper Diagnostic's mandate ends.
I believe the vast majority of indicators out there show you what everybody else sees. I've always been an indicator guy, I respect and cherish most indicators and I know a good indicator when I see it.
However, although most indicators are great works of art, their practicality is in most cases doubtful. Presenting great information is one thing, but providing an edge over the people you trade against is something different.
What Everybody Else Sees
The G.O.A.T. Scalper Diagnostics is based on indicators most of you have probably heard of and used:
Moving Averages (particularly the Kaufman Moving Average, among others)
ADX and DI
Bollinger Bands
Stochastic (particularly the Stochastic RSI)
Most traders should be well familiar with these classic indicators, they've provided the basis for online indicator trading for decades. But it's also true that due to how popular online trading has become all over the world, one is more and more unable to use these indicators successfully on lower timeframes.
Usually, more noteworthy success is achieved by going up in scale and discovering the timeframe where a particular indicator produces no false signals. Often times these timeframes range from bi-weekly to multi-month scale. In other words, consistently successful low timeframe trading and scalp trading in particular are now almost impossible using indicators.
Traders that dominate the scalping arena are big professional/institutional groups of traders, who have systematic access to the order books of most exchanges. This can be achieved one way or another, but not by individuals, small groups without significant capital or simply traders who lack political/social power and influence in the trading field.
In other words - giant order book traders have an edge over everybody else, who use indicators to trade on lower timeframes.
Through a series of interventions into these classical indicators, the G.O.A.T. System brings them back into the lower timeframe competitive game. Most original formulas are preserved, but these immortal classics are applied in ways popular TA would consider unorthodox.
Ingenious Indicators Built by Creators
The G.O.A.T. Scalper Diagnostics relies on the fundamental work of others. The System is developed on the basis of:
Quadratic Kernel Regression - it uses the publicly published library of Justin Dehorty: www.tradingview.com
PMARP - Price Moving Average Ratio & Percentile, publicly published by "The_Caretaker": www.tradingview.com
These Creators deserve full credit for their fundamental work and are endorsed by the G.O.A.T. Scalper Diagnostics project.
And yet... ingenious and inspired as these tools are, in my humble opinion the general public is presented with a rather unproductive way to apply them. In my own view, these wonderful tools built by JDehorty and The_Caretaker have a massive potential should they be applied and wielded in a different direction. So I tried to bring my vision about them into flesh with the G.O.A.T. Diagnostics.
What the G.O.A.T. Scalper Diagnostics Is and How to Use It
It's a System for new pattern discovery, bringing the disciplines of pattern and indicator trading together.
By using it as a stand-alone, or mixing it with other great indicators, one is able to discover new indicator patterns. Patterns can be compared, matched together and categorized. By applying statistics to differentiated historical pattern groups, we're able to derive their meaning.
Thus, the trader is able to research their own "alphabet" to read the price charts. After categorizing and differentiating pattern groups with statistically predominant meaning, the trader is then able to read into longer scenarios - price set-ups that are harder to detect due to them being stretched in time or misshapen according to the particular situation.
The G.O.A.T. Scalper leverages and encourages group trading, as different traders will probably discover different price "alphabets" for themselves, potentially giving rise to a social economy of sharing and combining "trading languages" based on indicator patterns people have discovered via the G.O.A.T. Diagnostics.
Support/Resistance Trading
The G.O.A.T. Scalper has its own way of deriving Support/Resistance.
Unlike most existing S/R indicators, The Scalper derives Support/Resistance not by measuring price highs, lows and closes, but solely by using momentum and trend strength.
This seems like a much more versatile way to plot S/R during scalping on low timeframes where time is of essence and the trader's view is too narrow to have macro S/R levels in constant consideration.
The Scalper's way to derive S/R in real time and on the go, while staying very relative to important higher timeframe S/R zones, makes it much more desirable than any other S/R indicator I've thus far encountered.
All S/R functionality is derived from the classical ADX and DI indicator. To do this, I use the ADX and DI in an unpopular way. To generate the actual plot of S/R levels I also modify the indicator's code, not by removing functional parts from it, but adding more to it in order to filter the signals it produces.
I can metaphorically describe its action in the following way:
Imagine you're Price action itself;
You're walking through a labyrinth or corridors. You're walking through one straight corridor, and it has a crossing with another corridor ahead;
Very strong wind is blowing along that other corridor. You can't see the wind, but when you reach it and try to move past it, the force of the wind resists your moving ahead and instead pushes you sideways.
At this point, the G.O.A.T. Diagnostics already knows this can only be one thing - resistance.
Orthodox TA and trading demand retests. In my opinion, this deeply rooted tradition wastes time proving the obvious, then wastes time again double-proving the validity of recent past, while scalping opportunities go to waste. Modern successful traders are way ahead of the popular strategy of testing and retesting S/R that almost every trader uses. So-called "Stops hunting" is just one expression of this situation, where wide adoption of the S/R retesting strategy actually lures unsuccessful traders into the schemes of the successful few.
In my own way of trading, I use the G.O.A.T. Diagnostics to take action on Support/Resistance as it's plotted in real time.
But probably my biggest heresy into the DI is my opinion, that the crossings of the +DI and -DI are useless and should actually be discarded.
My research shows that the DIs often show indications of being "oversold", but don't seem to exhibit an "overbought" state. Statistically, I've had much more success basing my TA on that, rather than cross-ups and cross-downs of the DI plot lines.
Therefore I discarded these crossings by presenting the DI part of the ADX and DI as a Heatmap channel rather than crossing lines.
To further enhance the ability of the System to provide S/R analysis, I plot this Heatmap onto an adjustable price offset plots (a percentage above and below current price).
In modern times, the vast majority of trading is done by automatic machines and algorithms. To give a specific example, one can easily notice, that a 5% offset of the BTC 1h price plot leads to remarkably accurate S/R charting. Following the rule to chart a S/R line connecting highs and lows on the 5% price offset often successfully "foresees" valid S/R zones before price ever visits them. Or, the levels were visited so far back in the timeframe's history that orthodox understanding considers them "invalidated" or washed away in the noise of the relevant volume profile.
My explanation for this is simple - I think Grid bots now dominate automatic trading across the majority of exchanges.
In my understanding, by adjusting the percentage offset of current price action I can often discover relevant conglomerations of dominating Grid bot cell parameters and anticipate price reaction. By plotting the DI heatmap on these price action offsets I can use the indicator for my trading decisions.
Heatmaps
Every heatmap produces different series of data. They're not the same.
Bollinger Band heatmap depicts the percentile distance between the Band's extremes.
The price candles heatmap, and the KAMA moving average heatmap, depict the percentile distance between price and the KAMA. So, it's the same thing. However, the percentile of that distance is calculated in two different ways, hence the difference in color in every particular moment. This color discrepancy aims to visualize the "strain" between price action and KAMA, like a soft and hard "springs" that go in unison with each other in sustainable moves, and in dissonance with each other during unsustainable moves.
Price offset heatmap depicts the percentile average of the +DI (above price) and the -DI (below price). A Hot temperature above price and a Cold temperature below price would mean a strong bullish sentiment, and vise versa, while Green would mean neutrality in sentiment.
There are important interplays between different heatmaps. For example, although representing totally different things, a Teal price bar would almost always (according to historical statistics) foreshadow a change in DI's heatmap sentiment. That's just one avenue of correlation between S/R analysis and sentiment analysis using the G.O.A.T. Diagnostics.
Oscillator Chart
In terms of applying Quadratic Kernel Regression, I endorse the natural principle that no center can exist without a periphery, and no periphery can exist without a center. Therefore I try to pay attention not only to the average of the regression's values, but also to the cloud of data points itself.
Following this understanding, I attempt to depict the natural cycles of price converging/diverging towards/from its regression average. To do this, I apply the classic Stochastic formula.
Thus, the Oscillator part of the System depicts the following:
Thin heatmap line displays the cycles of price converging with its quadratic kernel regression average (moving down), and diverging with its regression average (moving up). Its heatmap depicts the percentile of this oscillation.
The wider heatmap line displays the KAMA's cycles of convergence/divergence with its own quadratic kernel regression average. The reason for this is again creating discrepancy - while KAMA is based on price action, its regression data values differ from those of price action's regression. This discrepancy produces useful historic patterns that can be studied statistically.
The thin and wide purple oscillator lines depict the change of slope of price action regression average and KAMA regression average, respectively. Very often change of slope is not detectable with the naked eye, but clearly indicated by the oscillators.
By combining all these elements into a single analysis, a trader can detect hidden trends that are yet to become visible for the rest of market participants.
For example, convergence of price with its quadratic kernel regression average while the slope of the average deteriorates down in most cases (according to statistics) means a sideways consolidation in a downtrend before downtrend continuation. Conversely, deviation of price action from its regression average while the regression average slope deteriorates down usually marks the very beginning of a downtrend.
Bollinger Bands
Bollinger Bands are not modified, but are based on quadratic kernel regression values. Thus, if Bollinger Bands themselves are indicative of volatility, then based on kernel regression values, they should indicate the volatility of change of values in the regression's window.
Again, applying it to both the price and KAMA regression data series, a discrepancy is highlighted that leads to useful historical patterns subject to analysis and categorization.
SOME EXAMPLES
Support / Resistance
Support/Resistance levels are market by White Triangles with dotted lines plotted from them, in real time. The indicator plots Ghost Triangles in anticipation of Support/Resistance, preparing the trader for the eventual confirmation of a zone of interest and signaling price is feeling Support or Resistance pressure.
Dialing the length of the S/R lines to 25 makes the indicator more useful.
Dialing the setting to 500 clearly shows macro S/R zones by conglomerating and bundling individual lines. The thicker the bundling and the confluence of lines, the more significant the zone.
Thus lower timeframe scalping and trading is made more easy, without the need to do nearly as much manual S/R charting. Support/Resistance analysis and plotting is entirely based on a modified ADX.
Heatmap
Sustainable moves are generally marked by Green price color and calm KAMA colors.
Unsustainable moves are usually marked by more extreme colors of price bars and KAMA. Red usually means price is unsustainably distanced from the KAMA, while deep Blue usually means price is undesirably close to the KAMA, foreshadowing a directional distancing.
Usually Teal color of price bars and KAMA foreshadow a change of sentiment of the outside Heatmap sentiment channel.
Red color of the outside channel always signals the direction of the desired sentimental movement, while Blue signals the extent at which the counter-element suffers. Thus, one side being Green, while the other is Blue, often means the Blue will soon evolve into a warmer color, attracting price in that direction. Outside Heatmap channel is entirely based on a modified DI.
Oscillator Chart
An example of Chart Diagnosis using the Oscillator and other elements of the G.O.A.T. Scalper:
First (far left), a Resistance is plotted. This coincides with price bars being Red (distressed state). The thin colorful Oscillator line takes an Up-turn, signifying a period of price moving away from its Quadratic Kernel Regression (pink moving average).
After Price cools down to Green sustainable colors, a Support is plotted. During this time, the thin colorful line is falling down, signifying a period when the distance between price action and its quadratic kernel regression average is decreasing.
During this phase, the thin purple Oscillator line goes up. This signifies the slope of the price regression is restoring to the upside.
Next, the thin colorful line starts going up again, signifying another period of price getting further away from its regression average. This time to the upside.
Resistance is being broken and new support is established. At this point, the thin colorful line starts falling again, signifying distance between price and its regression MA is shortening. This is clearly visible as a sideways consolidation (with a slight tilt up of slope).
A moment comes when all lines - the price and KAMA lines, and price and KAMA regression slopes, all point down. A new down period is clearly starting. This is further indicated by Teal price bars and new Resistance forming. Notice how the external heatmap channel goes into more balanced Green colors with trend enthusiasm calming down.
This analysis may appear to be overwhelming and confusing at first, as these metrics are unorthodox and unpopular. But different aspects of the indicator can be toggled ON/OFF to single them out, which makes observations much simpler for new users. After some time spent discovering personal patterns, or reviewing other users' catalogues with already published pattern libraries, it soon becomes easy to read charts in this new way.
Bollinger Bands
Bollinger Bands provide another way to produce patterns that give users specific chart information.
One noteworthy indication is when the price and KAMA Bollinger Bands separate their value zones. Since the zones of these Bands are based on the kernel regression values of the respective sources, their separation is significant and too often means violent reversals or violent continuations (which usually can be judged using the other metrics the System provides, or additional indicators of choice).
Another noteworthy Bollinger Band pattern is when price action leaves a prolonged trending move.
First phase of the end of a prolonged trending move is the BB zones expanding and doing a significant overlap.
Second stage is price getting reaccepted in the Price BB. This however doesn't mean reacceptance in the KAMA BB and if the moment isn't right, usually leads to bounces and continuations.
The KAMA needs to "make space" for price to get reaccepted into the KAMA BB. While the KAMA is outside its BB or very near to its wall, price reacceptance into it is not very probable. When KAMA withdraws from its BB wall, opening an "entrance on its membrane", that's when price is eligible to get reaccepted into the KAMA BB. That's usually the moment the long awaited consolidation starts and a long trending move is over.
Users of the G.O.A.T. Scalper Diagnostics can discover many more patterns and correlations between patterns within the System. But the System itself can multiply all possible patterns when inspected in the context of additional indicators, leading to vast possibilities of signal and pattern discovery with huge potential.
A very good idea would probably be to use the G.O.A.T. Diagnostics together with the Ichimoku.
Ichimoku has always been famous for its genius simplicity and elegant profoundness, but notorious for its total lack of accuracy, as well as general uselessness on lower timeframes. The G.O.A.T. System has the potential to enhance all of Ichimoku's strengths and cure its weaknesses.
Yet another good idea may be to pair it with kindred indicators, like the Gaussian Channel, which has a stunning performance, but suffers from too high level of generalization. The Diagnostics can provide the intricate texture of price manoeuvres the Gaussian Channel fails to register, while the GC can give the Scalper even more solid context for its patterns.
The worthwhile possibilities seem endless...
Entry Table
I've added a little Entry Table at the bottom right corner. It's designed to potentially help scalpers trade faster, and to visualize a potential trade they're thinking about before they execute it. A Stop Loss is visually plotted in real time to better visualize it's placement in the chart context.
It encourages responsible risk management in its settings:
The user enters the amount of their trading portfolio;
Then specify the percentage of their portfolio they're willing to risk at every trade;
After that the user can chose to specify a flat percentage Stop Loss.
The table will calculate the size of the entry of a market order, so the user only risks the specified percentage of their portfolio should the specified Stop Loss level is hit.
There's also the option to use automatically suggested Stop Loss, based on recent volatility. The actual Stop Loss is calculated 20% away from the actual volatility level, to better protect from unforeseen wicks.
In the current example, the user with a $1000 trading portfolio has to do a $1000 entry to lose 1% of their portfolio ($10) at a 1% Stop Loss.
But the user has to do a $2,525 entry in order to lose 1% of their portfolio (%10) at a much closer Stop Loss which is less than 1%, based on recent volatility.
The Entry Table should be considered as a cosmetic convenience and not a dedicated risk management tool.
CONCLUSION:
The G.O.A.T. Scalper Diagnostics is an indicator System, based on popular, but modified and tweaked versions of indicators like the ADX and DI, Stochastic, Bollinger Bands and MAs. It also leverages the remarkable work of inspired creators: JDehorty's Quadratic Kernel Regression library, and The_Caretaker's PMARP .
The G.O.A.T. Scalper Diagnostics indicator system enables users to discover so-called new "indicator-pattern alphabets", reading price charts in new and unorthodox ways, thus gaining an edge over the majority of market participants they trade against.
The high degree of freedom when discovering new patterns, either within the System itself or correlating its output to external auxiliary indicators, highlights the System's potential for original discoveries leading to highly personalized trading strategies. Exchanging information about personal pattern libraries can potentially also give birth to new private trading communities.
